Deep learning-mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms.

Abstract

Concealed accessory pathway (AP) may cause atrial ventricular reentrant tachycardia impacting the health of patients. However, it is asymptomatic and undetectable during sinus rhythm. To detect concealed AP with electrocardiography (ECG) images, we collected normal sinus rhythmic ECG images of concealed AP patients and healthy subjects. All ECG images were randomly allocated to the training and testing datasets, and were used to train and test six popular convolutional neural networks from ImageNet pre-training and random initialization, respectively. We screened 152 ECG recordings in concealed AP group and 600 ECG recordings in control group. There were no statistically significant differences in ECG characteristics between control group and concealed AP group in terms of PR interval and QRS interval. However, the QT interval and QTc were slightly higher in control group than in concealed AP group. In the testing set, ResNet26, SE-ResNet50, MobileNetV3_large_100, and DenseNet169 achieved a sensitivity rate more than 87.0% with a specificity rate above 98.0%. And models trained from random initialization showed similar performance and convergence with models trained from ImageNet pre-training. Our study suggests that deep learning could be an effective way to predict concealed AP with normal sinus rhythmic ECG images. And our results might encourage people to rethink the possibility of training from random initialization on ECG image tasks.